DMAP: Human-Aligned Structural Document Map for Multimodal Document Understanding

TL;DR

This paper introduces DMAP, a hierarchical and relational document representation that improves multimodal question-answering by aligning with human understanding and enabling structure-aware reasoning.

Contribution

The paper presents a novel hierarchical document map (DMAP) that encodes structural and relational information, enhancing multimodal document understanding and reasoning.

Findings

DMAP improves retrieval precision and reasoning consistency.

DMAP outperforms conventional RAG-based approaches on MMDocQA benchmarks.

The approach aligns document representations with human interpretive patterns.

Abstract

Existing multimodal document question-answering (QA) systems predominantly rely on flat semantic retrieval, representing documents as a set of disconnected text chunks and largely neglecting their intrinsic hierarchical and relational structures. Such flattening disrupts logical and spatial dependencies - such as section organization, figure-text correspondence, and cross-reference relations, that humans naturally exploit for comprehension. To address this limitation, we introduce a document-level structural Document MAP (DMAP), which explicitly encodes both hierarchical organization and inter-element relationships within multimodal documents. Specifically, we design a Structured-Semantic Understanding Agent to construct DMAP by organizing textual content together with figures, tables, charts, etc. into a human-aligned hierarchical schema that captures both semantic and layout dependencies. Building upon this representation, a Reflective Reasoning Agent performs structure-aware and evidence-driven reasoning, dynamically assessing the sufficiency of retrieved context and iteratively refining answers through targeted interactions with DMAP. Extensive experiments on MMDocQA benchmarks demonstrate that DMAP yields document-specific structural representations aligned with human interpretive patterns, substantially enhancing retrieval precision, reasoning consistency, and multimodal comprehension over conventional RAG-based approaches. Code is available at https://github.com/Forlorin/DMAP